The present invention relates to product checkout devices and more specifically to a produce recognition method.
Bar code readers are well known for their usefulness in retail checkout and inventory control. Bar code readers are capable of identifying and recording most items during a typical transaction since most items are labeled with bar codes.
Items which are typically not identified and recorded by a bar code reader are produce items, since produce items are typically not labeled with bar codes. Bar code readers may include a scale for weighing produce items to assist in determining the price of such items. But identification of produce items is still a task for the checkout operator, who must identify a produce item and then manually enter an item identification code. Operator identification methods are slow and inefficient because they typically involve a visual comparison of a produce item with pictures of produce items, or a lookup of text in table. Operator identification methods are also prone to error, on the order of fifteen percent.
A produce recognition system is disclosed in the cited co-pending application. A produce item is placed over a window in a spectral data collector, the produce item is illuminated, and the spectrum of the diffuse reflected light from the produce item is measured. A terminal compares the spectrum to reference spectra in a library to determine a list of candidate identifications.
Finding an appropriate length for the list is important in order to achieve optimal speed without sacrificing accuracy. If the list is too long, the operator may take longer than necessary to find a matching candidate. If the list is too short, the matching candidate could be left out, leaving the operator unable to find it. The operator may also choose incorrectly.
Therefore, it would be desirable to provide a produce recognition method with improved selection speed and accuracy.
In accordance with the teachings of the present invention, a produce recognition method is provided.
The method includes the steps of obtaining produce data associated with a produce item, determining distances between the produce data and reference produce data, determining confidence values from the distances, determining first confidence values which are greater than a threshold confidence value, displaying candidate identifications associated with the first confidence values, and recording an operator choice of one of the candidate identifications.
It is accordingly an object of the present invention to provide a produce recognition method.
It is another object of the present invention to provide a method of improving selection speed and accuracy of produce choices in a produce recognition system.
It is another object of the present invention to determine an optimal number of candidate identifications in a candidate identification list.